The Maverick Basin of south Texas is currently undergoing active exploration and production of gas and oil from tight sandstone reservoirs. The most productive tight sandstones in the basin are in the Upper Cretaceous San Miguel, Olmos, and Escondido Formations. These units are second only to the Eagle Ford Shale in terms of cumulative production volumes. The structural history of the Maverick Basin, from rifting to subsidence to exhumation, has had a profound effect on the characteristics of these reservoirs and the petroleum resources contained therein. This U.S. Geological Survey review of the production history of these strata reflects a recent shift from conventional production to horizontal drilling (unconventional) that exploits low permeability reservoirs in previously overlooked areas of existing oil and gas fields in southern Texas, typically outside of established field boundaries.
To investigate the physical properties of the Maverick Basin hydrocarbon accumulations, this case study compiled American Petroleum Institute (API) gravity measurements and calculated cumulative gas/oil ratios (GOR) for thousands of producing wells from the San Miguel, Olmos, and Escondido Formations. Maps were generated from the compiled well production data to show the spatial heterogeneity of API gravity and GOR values for the three formations within the Maverick Basin and immediately outside the basin to the northeast. Within the Maverick Basin, the spatial patterns of API gravity values indicate lighter oils downdip towards the southern basin edge. GOR values indicative of wet and dry gases within the basin are seen interspersed, with values that correspond to black and heavy oils. Differences in the spatial patterns of the petroleum properties within the Maverick Basin are interpreted as effects of Eocene basin inversion caused by Laramide orogenic deformation, and the resulting reservoir exhumation of basin strata. East of the Maverick Basin, spatial distributions of API gravity and GOR values show progressively heavier oils updip to the northwest, grading to dry gases downdip to the southeast, which correlates to the oil and gas windows of the underlying Eagle Ford Shale.
Correlation of API gravity and GOR values from the San Miguel, Olmos, and Escondido Formations with thermal maturity data from the Eagle Ford Shale suggests that the Eagle Ford Shale is the petroleum source, and that petroleum migration was approximately vertical for areas to the east of the Maverick Basin. The discontinuity of API gravity and GOR properties within the Maverick Basin implies a complex petroleum charge history, possibly involving the remigration of petroleum and the addition of petroleum from other source intervals in Mexico, to the southwest. Depressurization of exhumed, overpressured reservoirs of the San Miguel, Olmos, and Escondido Formations can explain the intermittent occurrence of gas production throughout the southern Maverick Basin by exsolution of gas from formation brines and the resulting dry gas flushing of hydrocarbon-charged reservoirs. The introduction of dry gas through flushing can, in turn, explain why the patterns of API gravity and GOR values are so dissimilar in the Maverick Basin. This process has implications for possible future production of unconventional resources from undiscovered tight-gas reservoirs in strata of the San Miguel, Olmos, and Escondido Formations, and a different approach to petroleum exploration may be needed in the Maverick Basin relative to exploration techniques applied in other basins within the northern Gulf of Mexico.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235124","programNote":"Energy Resources Program","usgsCitation":"Doolan, C.A., Craddock, W.H., Buursink, M.L., Hatcherian, J.J., and Cahan, S.M., 2024, Spatial distribution of API gravity and gas/oil ratios for petroleum accumulations in Upper Cretaceous strata of the San Miguel, Olmos, and Escondido Formations of the south Texas Maverick Basin—Implications for petroleum migration and charge history: U.S. Geological Survey Scientific Investigations Report 2023–5124, 24 p., https://doi.org/10.3133/sir20235124.","productDescription":"iv, 24 p.","numberOfPages":"24","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-133374","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true},{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":426494,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5124/coverthb2.jpg"},{"id":426498,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5124/sir20235124.XML"},{"id":426497,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5124/images/"},{"id":426496,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20235124/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":426495,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5124/sir20235124.pdf","text":"Report","size":"2.88 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5124"},{"id":499389,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_116208.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Texas","otherGeospatial":"Maverick Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -101.09222334413582,\n 30.33527822084403\n ],\n [\n -101.09222334413582,\n 26.628154158079013\n ],\n [\n -95.81878584413593,\n 26.628154158079013\n ],\n [\n -95.81878584413593,\n 30.33527822084403\n ],\n [\n -101.09222334413582,\n 30.33527822084403\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Geology, Energy & Minerals Science Center
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12201 Sunrise Valley Drive
Mail Stop 954
Reston, VA 20192
In 2014, the Pāhoa lava flow at Kīlauea, on the Island of Hawaiʻi (USA), entered a string of pre-existing meter-width ground cracks in the volcano’s East Rift Zone. The ground cracks transported lava below the surface in a direction discordant to the slope of the landscape. The cracks, which were 100s of meters long and 10s to 100s of meters deep, also widened by up to several meters as they filled, probably in part at the expense of adjacent cracks, which likely closed. Widening of the cracks caused shallow crustal blocks on the volcano’s flank to shift—this deformation was captured by a nearby GPS station and a borehole tiltmeter. The GPS station moved away from the cracks in response, while the tiltmeter showed tilting toward the cracks, consistent with opening. Noting that the lava-filled cracks act as top-fed dikes, we adapt existing theory for the thermo-rheological evolution of dikes to analyze transport of lava captured by ground cracks and propose mechanisms for the exit of the lava back to the surface. This study shows that ground cracks as narrow as 50 cm wide can facilitate the transport of advancing lava flows and can carry lava in directions that differ from those expected based on surface topography, invalidating flow path projections based on the assumption of subaerial flow.
The ~300-km-long eastern Tennessee seismic zone (ETSZ), USA, is the second-most seismically active region east of the Rocky Mountains. Seismicity generally occurs below the Paleozoic fold-and-thrust belt within the Mesoproterozoic basement, at depths of 5–26 km, and earthquake magnitudes during the instrumental record have been moment magnitude (Mw)≤4.8. Evidence of surface deformation may not exist or be difficult to detect because of the vegetated and soil-mantled landscape, landslides, locally steep topography, anthropogenic landscape modification, or long, irregular recurrence intervals between surface-rupturing earthquakes. Despite the deep seismicity, analog models indicate that accumulation of strike-slip or oblique-slip displacement at depth could be expected to propagate upward through the Paleozoic section, producing a detectable surficial signal of distributed faulting. To identify potential surface deformation, we interrogated the landscape at different spatial scales. We evaluated morphotectonic and channel metrics, such as channel sinuosity and catchment-scale hypsometry. Additionally, we mapped possible fault-related topographic features on 1-m lidar. Finally, we integrated our observations with available bedrock and Quaternary surficial mapping and subsurface geophysical data. At a regional scale, most morphotectonic and channel metrics have a strong lithologic control. Within smaller regions of similar lithology, we observe changes in landscape metrics like channel sinuosity and catchment-scale hypsometry that spatially correlate with new lineaments identified in this study and previously mapped east–west Cenozoic faults. These faults have apparent left-lateral offsets, are optimally oriented to slip in the current stress field, and match kinematics from recent focal mechanisms, but do not clearly preserve evidence of late Pleistocene or Holocene tectonic surface deformation. Most newly mapped lineaments might be explained by either tectonic or non-tectonic origins, such as fluvial or karst processes. We also re-evaluated a previously described paleoseismic site and interpret that the exposure does not record evidence of late Pleistocene faulting but instead is explained by fluvial stratigraphy.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120230094","usgsCitation":"Jobe, J.A., Briggs, R.W., Gold, R.D., Bauer, L., and Collett, C., 2024, Limited evidence of late Quaternary tectonic surface deformation in the eastern Tennessee seismic zone, USA: Bulletin of the Seismological Society of America, v. 114, no. 4, p. 1920-1940, https://doi.org/10.1785/0120230094.","productDescription":"21 p.","startPage":"1920","endPage":"1940","ipdsId":"IP-154193","costCenters":[{"id":78686,"text":"Geologic Hazards Science Center - Seismology / Geomagnetism","active":true,"usgs":true}],"links":[{"id":492246,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Tennessee","otherGeospatial":"eastern Tennessee","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -86.17028696488822,\n 36.643021837336434\n ],\n [\n -86.17028696488822,\n 35.0924378490018\n ],\n [\n -82.53465655915278,\n 35.0924378490018\n ],\n [\n -82.53465655915278,\n 36.643021837336434\n ],\n [\n -86.17028696488822,\n 36.643021837336434\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"114","issue":"4","noUsgsAuthors":false,"publicationDate":"2024-03-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Thompson Jobe, Jessica A. 0000-0001-5574-4523","orcid":"https://orcid.org/0000-0001-5574-4523","contributorId":295377,"corporation":false,"usgs":true,"family":"Thompson Jobe","given":"Jessica","middleInitial":"A.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":943168,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Briggs, Richard W. 0000-0001-8108-0046 rbriggs@usgs.gov","orcid":"https://orcid.org/0000-0001-8108-0046","contributorId":4136,"corporation":false,"usgs":true,"family":"Briggs","given":"Richard","email":"rbriggs@usgs.gov","middleInitial":"W.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":943169,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gold, Ryan D. 0000-0002-4464-6394 rgold@usgs.gov","orcid":"https://orcid.org/0000-0002-4464-6394","contributorId":3883,"corporation":false,"usgs":true,"family":"Gold","given":"Ryan","email":"rgold@usgs.gov","middleInitial":"D.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":943170,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bauer, Laurel","contributorId":266056,"corporation":false,"usgs":false,"family":"Bauer","given":"Laurel","email":"","affiliations":[{"id":54872,"text":"Office of Nuclear Reactor Regulation, U.S. Nuclear Regulatory Commission","active":true,"usgs":false}],"preferred":false,"id":943171,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Collett, Camille 0000-0003-4836-0243","orcid":"https://orcid.org/0000-0003-4836-0243","contributorId":310393,"corporation":false,"usgs":false,"family":"Collett","given":"Camille","affiliations":[{"id":67175,"text":"Formerly: U.S. Geological Survey, Geologic Hazards Science Center","active":true,"usgs":false}],"preferred":false,"id":943172,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70257434,"text":"70257434 - 2024 - White-tailed deer detection rates increase when coyotes are present","interactions":[],"lastModifiedDate":"2024-09-06T16:44:01.482689","indexId":"70257434","displayToPublicDate":"2024-03-17T11:37:49","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1467,"text":"Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"White-tailed deer detection rates increase when coyotes are present","docAbstract":"Predator species can indirectly affect prey species through the cost of anti-predator behavior responses, which may involve shifts in occupancy, space use, or movement. Quantifying the various strategies implemented by prey species to avoid adverse interactions with predators can lead to a better understanding of potential population-level repercussions. Therefore, the purpose of this study was to examine predator–prey interactions by quantifying the effect of predator species presence on detection rates of prey species, using coyotes (Canis latrans) and white-tailed deer (Odocoileus virginianus) in Central Appalachian forests of the eastern United States as a model predator–prey system. To test two competing hypotheses related to interspecific interactions, we modeled species detections from 319 camera traps with a two-species occupancy model that incorporated a continuous-time detection process. We found that white-tailed deer occupancy was independent of coyote occupancy, but white-tailed deer were more frequently detectable and had greater detection intensity at sites where coyotes were present, regardless of vegetation-related covariates. In addition, white-tailed deer detection rates at sites with coyotes were highest when presumed forage availability was relatively low. These findings suggest that white-tailed deer may be exhibiting an active avoidance behavioral response to predators by increasing movement rates when coyotes are present in an area, perhaps due to reactive evasive maneuvers and/or proactive attempts to reduce adverse encounters with them. Concurrently, coyotes could be occupying sites with higher white-tailed deer densities. Because white-tailed deer did not exhibit significant shifts in daily activity patterns based on coyote occupancy, we further suggest that white-tailed deer in our study system generally do not use temporal partitioning as their primary strategy for avoiding encounters with coyotes. Overall, our study implements a recently developed analytical approach for modeling multi-species occupancy from camera traps and provides novel ecological insight into the complex relationships between predator and prey species.
","language":"English","publisher":"Wiley","doi":"10.1002/ece3.11149","usgsCitation":"Clipp, H.L., Pesi, S.M., Miller, M.L., Gigliotti, L., Skelly, B.P., and Rota, C., 2024, White-tailed deer detection rates increase when coyotes are present: Ecology and Evolution, v. 14, no. 3, e11149, 13 pp., https://doi.org/10.1002/ece3.11149.","productDescription":"e11149, 13 pp.","ipdsId":"IP-157302","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":440105,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.11149","text":"Publisher Index Page"},{"id":433571,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Pennsylvania, West Virginia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -80.4877790212181,\n 39.94978819373756\n ],\n [\n -82.01878727948417,\n 39.01317775882444\n ],\n [\n -82.58830516880357,\n 38.2047845680037\n ],\n [\n -79.5468825034643,\n 37.66652638635898\n ],\n [\n -77.96682473836029,\n 39.5141262945331\n ],\n [\n -76.37907636338417,\n 40.11338027059526\n ],\n [\n -77.28462588707731,\n 41.09189702989761\n ],\n [\n -80.4877790212181,\n 39.94978819373756\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"14","issue":"3","noUsgsAuthors":false,"publicationDate":"2024-03-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Clipp, Hannah L.","contributorId":342785,"corporation":false,"usgs":false,"family":"Clipp","given":"Hannah","email":"","middleInitial":"L.","affiliations":[{"id":12432,"text":"West Virginia University","active":true,"usgs":false}],"preferred":false,"id":910392,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pesi, Sarah M.","contributorId":342786,"corporation":false,"usgs":false,"family":"Pesi","given":"Sarah","email":"","middleInitial":"M.","affiliations":[{"id":12432,"text":"West Virginia University","active":true,"usgs":false}],"preferred":false,"id":910393,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Miller, Madison L.","contributorId":342787,"corporation":false,"usgs":false,"family":"Miller","given":"Madison","email":"","middleInitial":"L.","affiliations":[{"id":12432,"text":"West Virginia University","active":true,"usgs":false}],"preferred":false,"id":910394,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gigliotti, Laura C. 0000-0002-6390-4133","orcid":"https://orcid.org/0000-0002-6390-4133","contributorId":200327,"corporation":false,"usgs":false,"family":"Gigliotti","given":"Laura C.","affiliations":[],"preferred":false,"id":910395,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Skelly, Brett P.","contributorId":342789,"corporation":false,"usgs":false,"family":"Skelly","given":"Brett","email":"","middleInitial":"P.","affiliations":[{"id":12432,"text":"West Virginia University","active":true,"usgs":false}],"preferred":false,"id":910396,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Rota, Christopher T.","contributorId":342791,"corporation":false,"usgs":false,"family":"Rota","given":"Christopher T.","affiliations":[{"id":12432,"text":"West Virginia University","active":true,"usgs":false}],"preferred":false,"id":910397,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70252441,"text":"70252441 - 2024 - Data-driven adjustments for combined use of NGA-East hard-rock ground motion and site amplification models","interactions":[],"lastModifiedDate":"2024-05-07T14:35:47.184916","indexId":"70252441","displayToPublicDate":"2024-03-17T08:31:39","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1436,"text":"Earthquake Spectra","active":true,"publicationSubtype":{"id":10}},"title":"Data-driven adjustments for combined use of NGA-East hard-rock ground motion and site amplification models","docAbstract":"Model development in the Next Generation Attenuation-East (NGA-East) project included two components developed concurrently and independently: (1) earthquake ground-motion models (GMMs) that predict the median and aleatory variability of various intensity measures conditioned on magnitude and distance, derived for a reference hard-rock site condition with an average shear-wave velocity in the upper 30 m (VS30) = 3000 m/s; and (2) a site amplification model that modifies intensity measures for softer site conditions. We investigate whether these models, when used in tandem, are compatible with ground-motion recordings in central and eastern North America (CENA) using an expanded version of the NGA-East database that includes new events from November 2011 (end date of NGA-East data curation) to April 2022. Following this expansion, the data set has 187 events, 2096 sites, and 16,272 three-component recordings, although the magnitude range remains limited (∼4 to 5.8). We compute residuals using 17 NGA-East GMMs and three data selection criteria that reflect within-CENA regional variations in ground-motion attributes. Mixed-effects regression of the residuals reveals a persistent pattern in which ground motions are overpredicted at short periods (0.01–0.6 s, including peak ground acceleration (PGA)) and underpredicted at longer periods. These misfits are regionally variable, with the Texas–Oklahoma–Kansas region having larger absolute misfits than other parts of CENA. Two factors potentially influencing these misfits are (1) differences in the site amplification models used to adjust the data to the reference condition during NGA-East GMM development relative to CENA amplification models applied since the 2018 National Seismic Hazard Model (NSHM), and (2) potential bias in simulation-based factors used to adjust ground motions from the hard-rock reference condition to a VS30 = 760 m/s condition. We provide adjustment factors and their epistemic uncertainties and discuss implications for applications.
","language":"English","publisher":"Sage Journals","doi":"10.1177/87552930241231825","usgsCitation":"Ramos-Sepulveda, M.E., Stewart, J.P., Parker, G.A., Moschetti, M.P., Thompson, E.M., Brandenberg, S.J., Hashash, Y.M., and Rathje, E., 2024, Data-driven adjustments for combined use of NGA-East hard-rock ground motion and site amplification models: Earthquake Spectra, v. 40, no. 2, p. 1132-1157, https://doi.org/10.1177/87552930241231825.","productDescription":"26 p.","startPage":"1132","endPage":"1157","ipdsId":"IP-153071","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":440108,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1177/87552930241231825","text":"Publisher Index 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\"}}]}","volume":"40","issue":"2","noUsgsAuthors":false,"publicationDate":"2024-03-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Ramos-Sepulveda, Maria E.","contributorId":294748,"corporation":false,"usgs":false,"family":"Ramos-Sepulveda","given":"Maria","email":"","middleInitial":"E.","affiliations":[{"id":13399,"text":"UCLA","active":true,"usgs":false}],"preferred":false,"id":897170,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stewart, Jonathan P.","contributorId":100110,"corporation":false,"usgs":false,"family":"Stewart","given":"Jonathan","email":"","middleInitial":"P.","affiliations":[{"id":7081,"text":"University of California - Los Angeles","active":true,"usgs":false}],"preferred":false,"id":897171,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Parker, Grace Alexandra 0000-0002-9445-2571","orcid":"https://orcid.org/0000-0002-9445-2571","contributorId":237091,"corporation":false,"usgs":true,"family":"Parker","given":"Grace","email":"","middleInitial":"Alexandra","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":897172,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Moschetti, Morgan P. 0000-0001-7261-0295 mmoschetti@usgs.gov","orcid":"https://orcid.org/0000-0001-7261-0295","contributorId":1662,"corporation":false,"usgs":true,"family":"Moschetti","given":"Morgan","email":"mmoschetti@usgs.gov","middleInitial":"P.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":897173,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Thompson, Eric M. 0000-0002-6943-4806 emthompson@usgs.gov","orcid":"https://orcid.org/0000-0002-6943-4806","contributorId":150897,"corporation":false,"usgs":true,"family":"Thompson","given":"Eric","email":"emthompson@usgs.gov","middleInitial":"M.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":897174,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Brandenberg, Scott J.","contributorId":303895,"corporation":false,"usgs":false,"family":"Brandenberg","given":"Scott","email":"","middleInitial":"J.","affiliations":[{"id":13399,"text":"UCLA","active":true,"usgs":false}],"preferred":false,"id":897175,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hashash, Youssef M A","contributorId":146595,"corporation":false,"usgs":false,"family":"Hashash","given":"Youssef","email":"","middleInitial":"M A","affiliations":[{"id":15289,"text":"University of Illinois, Ven Te Chow Hydrosystems Laboratory","active":true,"usgs":false}],"preferred":false,"id":897176,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Rathje, Ellen 0000-0002-4169-7153","orcid":"https://orcid.org/0000-0002-4169-7153","contributorId":197024,"corporation":false,"usgs":false,"family":"Rathje","given":"Ellen","email":"","affiliations":[],"preferred":false,"id":897177,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70252191,"text":"70252191 - 2024 - Flood of October 31 to November 3, 2019, East Canada Creek, West Canada Creek, and Sacandaga River Basins","interactions":[],"lastModifiedDate":"2024-03-19T13:23:25.892201","indexId":"70252191","displayToPublicDate":"2024-03-15T08:21:38","publicationYear":"2024","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":18,"text":"Abstract or summary"},"title":"Flood of October 31 to November 3, 2019, East Canada Creek, West Canada Creek, and Sacandaga River Basins","docAbstract":"Between October 31 and November 3, 2019, historic flooding in parts of the Mohawk Valley and southern Adirondack region resulted in one fatality, an estimated $33 million in damages, and the declaration of a state of emergency for 13 New York counties. Flooding resulted from high-intensity rainfall within a 24-hour period between October 31 and November 1, 2019, at the end of an October that had much higher rainfall than normal. In that 24-hour period, rainfall amounts in the most heavily affected parts of the region largely ranged from about 2 to 5 inches, but a maximum rainfall amount of 7.00 inches was recorded in Speculator, NY in Hamilton County. In this location, a rainfall of 7.00 inches in a 24-hour period is estimated to have between a 200- and 500-year recurrence interval or between a 0.5- and 0.2-percent chance of happening or being exceeded in any given year.\n\nThe most severe flooding from October 31 to November 3, 2019, was mainly in the East and West Canada Creek basins, which are within the Mohawk River basin, and the Sacandaga River basin, which is within the upper Hudson River basin. Flooding resulted in new peak streamflow records at eight of nine selected U.S. Geological Survey streamgages from the region, including at three streamgages that have been in operation for about 100 years. At East Canada Creek at East Creek, NY (01348000), flooding resulted in the second highest peak streamflow in its 71-year period of record. National Weather Service major flood stages were exceeded at the three streamgages in the region where National Weather Service flood stages have been established and were exceeded at Hinckley Reservoir at Hinckley, NY (01343600). Hinckley Reservoir has a drainage area of 372 square miles and regulates West Canada Creek streamflow about 31 miles upstream of West Canada Creek at Kast Bridge (01346000) and 3 miles downstream of West Canada Creek near Wilmurt, NY (01343060). \n\nIn West Canada Creek, downstream of Hinckley Reservoir, a distinct double peak of streamflow happened during the 2019 flood and was recorded at West Canada Creek at Kast Bridge, NY (01346000). A similar, but less distinct, double peak was recorded at Mohawk River near Little Falls, NY (01347000), which is located 14.8 miles downstream of West Canada Creek at Kast Bridge, NY (01346000). The first peak of the double peak was likely caused by inflows to West Canada Creek from unregulated tributaries downstream of Hinckley Reservoir, such as Cincinnati Creek, which drains a relatively large area of 48.5 square miles in the northwest corner of the West Canada Creek watershed. Cincinnati Creek was ungaged at the time, but in response to the flood, the U.S. Geological Survey, in cooperation with the New York State Canal Corporation, installed a gage at Cincinnati Creek at Barneveld, NY (01344795) that has been in operation since November 2022. The second peak of the double peak, which happened about a day after the first peak, likely resulted from regulated streamflow that passed through Hinckley Reservoir. At the other streamgages upstream of Hinckley Reservoir, single peak streamflows were recorded during the flood. More details on the nature of the flood of October 31 to November 3, 2019, including the historic context of the flood, and the results from flood-frequency analysis of six selected streamgages in the Mohawk Valley and southern Adirondack region, are discussed in Graziano and others (2024).","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings of the 2024 Mohawk Watershed Symposium","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"Mohawk Watershed Symposium","conferenceDate":"March 15, 2024","conferenceLocation":"Schenectady, NY","language":"English","publisher":"Union College","collaboration":"New York State Department of Environmental Conservation","usgsCitation":"Graziano, A.P., Smith, T.L., and Lilienthal, A.G., 2024, Flood of October 31 to November 3, 2019, East Canada Creek, West Canada Creek, and Sacandaga River Basins, in Proceedings of the 2024 Mohawk Watershed Symposium, v. 14, Schenectady, NY, March 15, 2024, p. 35-40.","productDescription":"6 p.","startPage":"35","endPage":"40","ipdsId":"IP-162519","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":426768,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":426765,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://minerva.union.edu/garverj/mws/2024/symposium.html","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"New York","otherGeospatial":"East Canada Creek, West Canada Creek, and Sacandaga River Basins","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -74.26955336733255,\n 43.83029414607452\n ],\n [\n -75.24342943210101,\n 43.48996180099857\n ],\n [\n -75.21373328157367,\n 43.29245297551128\n ],\n [\n -74.29992522793536,\n 43.507816029663445\n ],\n [\n -74.26955336733255,\n 43.83029414607452\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"14","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Graziano, Alexander P. 0000-0003-1978-0986","orcid":"https://orcid.org/0000-0003-1978-0986","contributorId":211607,"corporation":false,"usgs":true,"family":"Graziano","given":"Alexander","email":"","middleInitial":"P.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":896878,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Smith, Travis L. 0000-0002-3448-2787 tlsmith@usgs.gov","orcid":"https://orcid.org/0000-0002-3448-2787","contributorId":297400,"corporation":false,"usgs":true,"family":"Smith","given":"Travis","email":"tlsmith@usgs.gov","middleInitial":"L.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":896879,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lilienthal, Arthur G. III 0000-0002-2906-6375","orcid":"https://orcid.org/0000-0002-2906-6375","contributorId":211366,"corporation":false,"usgs":true,"family":"Lilienthal","given":"Arthur","suffix":"III","email":"","middleInitial":"G.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":896880,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70252199,"text":"70252199 - 2024 - What the cliffs near America’s earliest settlements tell us about climate change","interactions":[],"lastModifiedDate":"2024-03-20T11:40:06.888832","indexId":"70252199","displayToPublicDate":"2024-03-14T06:38:12","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9348,"text":"Frontiers for Young Minds","active":true,"publicationSubtype":{"id":10}},"title":"What the cliffs near America’s earliest settlements tell us about climate change","docAbstract":"Climate change is a big problem for natural habitats, people, and the systems that support society, including roads, water supply, electrical grids, and phone and internet connections. It’s an important theme in politics, economics, and culture. Scientists make computer models to show what the climate might be like in the future, and it looks very different from what we are used to. Scientists can also learn about the future climate by looking at geological records from the past. About 3 million years ago, most of the US east coast was under water. Sediments that collected on the ocean floor show what the ocean environment was like back then. Some of these sediments are now exposed above water as bluffs along the James River near colonial Jamestown in southeastern Virginia, and they hold many clues as to what Earth may look like in the future.","language":"English","publisher":"Frontiers","doi":"10.3389/frym.2024.1295235","usgsCitation":"Dowsett, H., and Robinson, M., 2024, What the cliffs near America’s earliest settlements tell us about climate change: Frontiers for Young Minds, v. 12, 1295235, https://doi.org/10.3389/frym.2024.1295235.","productDescription":"1295235","ipdsId":"IP-157282","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":440136,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://dx.doi.org/10.3389/frym.2024.1295235","text":"Publisher Index Page"},{"id":426791,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Virginia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -77.10416403891121,\n 37.42165789456314\n ],\n [\n -77.10416403891121,\n 36.82153161972495\n ],\n [\n -76.33509978860388,\n 36.82153161972495\n ],\n [\n -76.33509978860388,\n 37.42165789456314\n ],\n [\n -77.10416403891121,\n 37.42165789456314\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"12","noUsgsAuthors":false,"publicationDate":"2024-03-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Dowsett, Harry J. 0000-0003-1983-7524","orcid":"https://orcid.org/0000-0003-1983-7524","contributorId":316789,"corporation":false,"usgs":true,"family":"Dowsett","given":"Harry J.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":896911,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Robinson, Marci M. 0000-0002-9200-4097","orcid":"https://orcid.org/0000-0002-9200-4097","contributorId":261664,"corporation":false,"usgs":true,"family":"Robinson","given":"Marci M.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":896912,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70252861,"text":"70252861 - 2024 - Sulphide petrology and ore genesis of the stratabound Sheep Creek sediment-hosted Zn–Pb–Ag–Sn prospect, and U–Pb zircon constraints on the timing of magmatism in the northern Alaska Range","interactions":[],"lastModifiedDate":"2024-09-23T15:25:30.720271","indexId":"70252861","displayToPublicDate":"2024-03-12T07:06:27","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1168,"text":"Canadian Journal of Earth Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Sulphide petrology and ore genesis of the stratabound Sheep Creek sediment-hosted Zn–Pb–Ag–Sn prospect, and U–Pb zircon constraints on the timing of magmatism in the northern Alaska Range","docAbstract":"The karstic Edwards and Trinity aquifers are classified as major sources of water in south-central Texas by the Texas Water Development Board. During 2019–23 the U.S. Geological Survey, in cooperation with the Edwards Aquifer Authority, mapped and described the geology and hydrostratigraphy of the rocks composing the Edwards and Trinity aquifers within parts of Bandera and Kendall Counties from field observations of the surficial expressions of the rocks. The thicknesses of the mapped lithostratigraphic and hydrostratigraphic units were also estimated from field observations in the study area.
The Cretaceous rocks in the study area are part of the Trinity Group and Edwards Group. The groups, formations, and members are composed primarily of layers of marls, shales, and limestones. The limestones are composed of mudstone through grainstone, framestone and boundstone, dolomite, and argillaceous and evaporitic rocks.
The principal structural feature in the study area is the Balcones fault zone. The Balcones fault zone is the result of late Oligocene and early Miocene extensional faulting and fracturing that was a result of the eastern Edwards Plateau uplift. In the Balcones fault zone, most of the faults in the study area are high-angle to vertical, en echelon, normal faults that are predominantly downthrown to the southeast.
Hydrostratigraphically, the rocks exposed in the study area are those that contain the Edwards aquifer, the upper zone of the Trinity aquifer, and the middle zone of the Trinity aquifer. Descriptions of the hydrostratigraphic units, thicknesses, hydrologic function, porosity types, and field identification and observations are provided, including those for the informal Bandera and Love Creek hydrostratigraphic units of the Edwards aquifer, which were identified through the mapping for this study.
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}\n }\n ]\n}","contact":"Director, Oklahoma-Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, TX 78754–4501
Middle Eocene to early Oligocene intrusions, widespread in the Ruby Mountains–East Humboldt Range metamorphic core complex, Nevada, USA, provide insights into a major Paleogene magmatic episode and its relation to tectonism in the northeastern Great Basin. These intrusions, well-exposed in upper Lamoille Canyon, range in composition from gabbro to leucomonzogranite. They form small plutons, sheets, and dikes that intrude the metamorphic and granitic infrastructure of the core complex. Two types of Paleogene monzogranite were recognized. The first is exemplified by two of the larger intrusive bodies, the Snow Lake Peak and Castle Lake intrusions, which occur as sheet-like bodies near and at the structural base of metamorphosed Neoproterozoic and Cambrian Prospect Mountain Quartzite where it is inverted above Cambrian and Ordovician marble of Verdi Peak in the Lamoille Canyon nappe. Swarms of dikes are associated with these intrusions. U-Pb (zircon) ages range ca. 40–33 Ma and typically display relatively simple and minor inheritance. The rocks have the lowest εHf (zircon) and εNd (whole rock) of any of the middle Cenozoic granites. The second type of monzogranite, Overlook type, typically occurs as thin, isolated dikes and leucosome-like bodies in Late Cretaceous granites of the infrastructure, with no obvious relationship to the large monzogranite bodies. Overlook-type monzogranite displays complex zircon inheritance, yields igneous ages ca. 37–32 Ma, and has εHf (zircon) and εNd (whole rock) identical to those of Late Cretaceous granites in the core complex. These isotopic and field data indicate that Overlook-type monzogranite formed in situ through anatexis of host Cretaceous granites. A pervasive thermal event was required to stimulate this crustal melting.
","language":"English","publisher":"Geological Society of America","doi":"10.1130/GES02673.1","usgsCitation":"Snoke, A., Barnes, C., Howard, K., Romanoski, A., Premo, W.R., Hetherington, C., Strike, A., Frost, C., Copeland, P., and Lee, S., 2024, Paleogene mid-crustal intrusions in the Ruby Mountains–East Humboldt Range metamorphic core complex, northeastern Nevada, USA: Geosphere, v. 20, no. 2, p. 577-620, https://doi.org/10.1130/GES02673.1.","productDescription":"44 p.","startPage":"577","endPage":"620","ipdsId":"IP-153033","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":440164,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1130/ges02673.1","text":"Publisher Index Page"},{"id":429247,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Nevada","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -117.21976098588017,\n 42.94002735616252\n ],\n [\n -117.21976098588017,\n 39.87569247364536\n ],\n [\n -113.15481957963019,\n 39.87569247364536\n ],\n [\n -113.15481957963019,\n 42.94002735616252\n ],\n [\n -117.21976098588017,\n 42.94002735616252\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"20","issue":"2","noUsgsAuthors":false,"publicationDate":"2024-03-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Snoke, A.W.","contributorId":336903,"corporation":false,"usgs":false,"family":"Snoke","given":"A.W.","affiliations":[{"id":36628,"text":"University of Wyoming","active":true,"usgs":false}],"preferred":false,"id":901374,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Barnes, C.B.","contributorId":336904,"corporation":false,"usgs":false,"family":"Barnes","given":"C.B.","email":"","affiliations":[{"id":36331,"text":"Texas Tech University","active":true,"usgs":false}],"preferred":false,"id":901375,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Howard, Keith A. 0000-0002-6462-2947","orcid":"https://orcid.org/0000-0002-6462-2947","contributorId":264832,"corporation":false,"usgs":true,"family":"Howard","given":"Keith A.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":901376,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Romanoski, A.","contributorId":336905,"corporation":false,"usgs":false,"family":"Romanoski","given":"A.","email":"","affiliations":[{"id":36331,"text":"Texas Tech University","active":true,"usgs":false}],"preferred":false,"id":901377,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Premo, Wayne R. 0000-0001-9904-4801 wpremo@usgs.gov","orcid":"https://orcid.org/0000-0001-9904-4801","contributorId":1697,"corporation":false,"usgs":true,"family":"Premo","given":"Wayne","email":"wpremo@usgs.gov","middleInitial":"R.","affiliations":[],"preferred":true,"id":901378,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hetherington, C.","contributorId":336906,"corporation":false,"usgs":false,"family":"Hetherington","given":"C.","email":"","affiliations":[{"id":36331,"text":"Texas Tech University","active":true,"usgs":false}],"preferred":false,"id":901379,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Strike, A.","contributorId":336909,"corporation":false,"usgs":false,"family":"Strike","given":"A.","email":"","affiliations":[{"id":80908,"text":"Lander, WY","active":true,"usgs":false}],"preferred":false,"id":901380,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Frost, C.","contributorId":336910,"corporation":false,"usgs":false,"family":"Frost","given":"C.","affiliations":[{"id":36628,"text":"University of Wyoming","active":true,"usgs":false}],"preferred":false,"id":901381,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Copeland, P.","contributorId":336912,"corporation":false,"usgs":false,"family":"Copeland","given":"P.","email":"","affiliations":[{"id":36391,"text":"University of Houston","active":true,"usgs":false}],"preferred":false,"id":901382,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Lee, S-Y","contributorId":336914,"corporation":false,"usgs":false,"family":"Lee","given":"S-Y","affiliations":[{"id":80909,"text":"CTL Group","active":true,"usgs":false}],"preferred":false,"id":901383,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70255084,"text":"70255084 - 2024 - Preparing for a Bsal invasion into North America has improved multi-sector readiness","interactions":[],"lastModifiedDate":"2024-06-12T15:33:58.8501","indexId":"70255084","displayToPublicDate":"2024-03-06T10:18:39","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":17102,"text":"Frontiers in Amphibian and Reptile Science","active":true,"publicationSubtype":{"id":10}},"title":"Preparing for a Bsal invasion into North America has improved multi-sector readiness","docAbstract":"Western palearctic salamander susceptibility to the skin disease caused by the amphibian chytrid fungus Batrachochytrium salamandrivorans (Bsal) was recognized in 2014, eliciting concerns for a potential novel wave of amphibian declines following the B. dendrobatidis (Bd) chytridiomycosis global pandemic. Although Bsal had not been detected in North America, initial experimental trials supported the heightened susceptibility of caudate amphibians to Bsal chytridiomycosis, recognizing the critical threat this pathogen poses to the North American salamander biodiversity hotspot. Here, we take stock of 10 years of research, collaboration, engagement, and outreach by the North American Bsal Task Force. We summarize main knowledge and conservation actions to both forestall and respond to Bsal invasion into North America. We address the questions: what have we learned; what are current challenges; and are we ready for a more effective reaction to Bsal’s eventual detection? We expect that the many contributions to preemptive planning accrued over the past decade will pay dividends in amphibian conservation effectiveness and can inform future responses to other novel wildlife diseases and extreme threats.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/famrs.2024.1347541","usgsCitation":"Olson, D., Campbell Grant, E.H., Bletz, M., Piovia-Scott, J., Lesbarreres, D., Kerby, J.L., Adams, M.J., Breitman, M.F., Christman, M.R., Forzán, M., Gray, M., Hill, A.J., Koo, M.S., Milenkaya, O., Rebollar, E.A., Rollins-Smith, L.A., Serr, M., Shepak, A., Shirose, L., Sprague, L., Walke, J., Warwick, A., and Mosher, B., 2024, Preparing for a Bsal invasion into North America has improved multi-sector readiness: Frontiers in Amphibian and Reptile Science, v. 2, :1347541, 21 p., https://doi.org/10.3389/famrs.2024.1347541.","productDescription":":1347541, 21 p.","ipdsId":"IP-159868","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":440205,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/famrs.2024.1347541","text":"Publisher Index 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USA","active":true,"usgs":false}],"preferred":false,"id":903356,"contributorType":{"id":1,"text":"Authors"},"rank":21},{"text":"Warwick, Alexa","contributorId":338560,"corporation":false,"usgs":false,"family":"Warwick","given":"Alexa","affiliations":[{"id":81160,"text":"Michigan State University, East Lansing, Michigan, USA","active":true,"usgs":false}],"preferred":false,"id":903357,"contributorType":{"id":1,"text":"Authors"},"rank":22},{"text":"Mosher, Brittany A.","contributorId":338561,"corporation":false,"usgs":false,"family":"Mosher","given":"Brittany A.","affiliations":[{"id":81161,"text":"University of Vermont, Burlington, Vermont","active":true,"usgs":false}],"preferred":false,"id":903358,"contributorType":{"id":1,"text":"Authors"},"rank":23}]}} ,{"id":70251901,"text":"70251901 - 2024 - Potential use of poultry farms by wild waterfowl in California's Central Valley varies across space, times of day, and species: implications for influenza transmission risk","interactions":[],"lastModifiedDate":"2024-10-23T15:51:57.505662","indexId":"70251901","displayToPublicDate":"2024-03-05T06:42:00","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Potential use of poultry farms by wild waterfowl in California's Central Valley varies across space, times of day, and species: implications for influenza transmission risk","docAbstract":"Interactions between wildlife and livestock can lead to cross-species disease transmission, which incurs economic costs and threatens wildlife conservation. Wild waterfowl are natural hosts of avian influenza viruses (AIVs), are often abundant near poultry farms, and have been linked to outbreaks of AIVs in poultry. Interspecific and seasonal variation in waterfowl movement and habitat use means that the risk of disease transmission between wild birds and poultry inevitably varies across species, space, and time. Here, we used GPS telemetry data from 10 waterfowl species in and near California's Central Valley, a region where both wild waterfowl and domestic poultry are abundant, to study selection of poultry farms by waterfowl across diel, seasonal, and annual cycles. We found that waterfowl selected for wetlands, open water, protected areas, and croplands, which meant that they generally avoided habitats that were likely to be used for poultry farming. These selection patterns were linked to species' ecology and diel behavioral patterns, such that avoidance of poultry habitats was stronger for local or partial migrants than for long-distance migrants, and stronger during daytime than at night. We then combined these habitat selection results with data on poultry farm locations to map risk of waterfowl–poultry contact across the Central Valley. Average selection strength at poultry farms was low, suggesting that current placement of poultry farms is generally effective for limiting risk of contact with wild birds. When we combined these habitat selection results with data on species' abundances and AIV infection prevalence, we found dramatic variation in potential AIV transmission risk among species. These results could be used to prioritize surveillance and biosecurity efforts for regions and times of relatively high risk. More generally, these results highlight that fine-scale movement data can help identify interspecific, seasonal, and diel patterns in animal behaviors that affect wildlife and poultry health.
Southern fox squirrels (Sciurus niger niger) have been declining due to habitat fragmentation, cover type conversion, and fire suppression in the Southeast. A decrease in growing season burns has led to hardwood encroachment and forest mesophication that benefit the competing eastern gray squirrels (S. carolinensis). In the southern Coastal Plain and Piedmont of Virginia, these pattern raises the question of whether gray squirrels are competitively excluding southern fox squirrels in these altered landscapes. From October 2019 to October 2020, we conducted continual camera trapping for southern fox squirrels and gray squirrels on the Big Woods/Piney Grove Complex (BWPGC) and at Fort Barfoot (FB) in the Coastal Plain and lower Piedmont of Virginia, respectively. Both sites are among the few areas that still contain large, intact pine savanna and mixed-pine hardwood forests in southeastern Virginia. We used two-species occupancy modeling to investigate occupancy estimates of southern fox squirrels and possible competition with gray squirrels, based on detection histories collected from camera traps on BWPGC and FB. We then conducted informed single-species occupancy modeling to estimate the necessary level-of-effort (LOE) required to determine the probable absence of southern fox squirrels at sampling sites in the region. No fox squirrels were observed at FB. Our top, two-species occupancy model showed that gray squirrel occupancy increased with increasing time since last burn. However, southern fox squirrel occupancy, in the absence of gray squirrels, decreased with increasing time since last burn. Gray squirrels typically inhabited hardwood-dominant closed canopy areas whereas southern fox squirrels did so at BWPGC only in the absence of gray squirrels. This suggests that southern fox squirrels are selecting areas on BWPGC based on resource needs and possibly competition with gray squirrels. A single-season occupancy model confirmed that southern fox squirrel occupancy decreased with time since the last burn. Our LOE analysis indicated that seven consecutive days of camera trapping without a detection would provide 90% confidence of the subspecies’ absence in areas burned two or more years prior to sampling. Southern fox squirrels may benefit from increased short-rotation burns to maintain or enhance pine-hardwood savannas and pine-hardwood savanna ecotones in southeastern Virginia.
","language":"English","publisher":"Southeastern Association of Fish and Wildlife Agencies","usgsCitation":"Guill, M., De La Cruz, J., Puckett, M., Klopfer, S., Martin, B., and Ford, W., 2024, Southern fox squirrel and eastern gray squirrel interactions in a fire-maintained ecosystem: Journal of the Southeastern Association of Fish and Wildlife Agencies, v. 11, p. 215-222.","productDescription":"8 p.","startPage":"215","endPage":"222","ipdsId":"IP-142691","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":481441,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://seafwa.org/journal/2024/southern-fox-squirrel-and-eastern-gray-squirrel-interactions-fire-maintained-ecosystem"},{"id":481465,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Virginia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -78,\n 37.1\n ],\n [\n -78,\n 36.9\n ],\n [\n -77,\n 36.9\n ],\n [\n -77,\n 37.1\n ],\n [\n -78,\n 37.1\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"11","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Guill, M.H.","contributorId":350233,"corporation":false,"usgs":false,"family":"Guill","given":"M.H.","affiliations":[{"id":25550,"text":"Virginia Polytechnic Institute and State University","active":true,"usgs":false}],"preferred":false,"id":925510,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"De La Cruz, J.L.","contributorId":349847,"corporation":false,"usgs":false,"family":"De La Cruz","given":"J.L.","affiliations":[{"id":81893,"text":"Virginia Polytechnic and State University","active":true,"usgs":false}],"preferred":false,"id":925511,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Puckett, M.","contributorId":350236,"corporation":false,"usgs":false,"family":"Puckett","given":"M.","affiliations":[{"id":56188,"text":"Virginia Department of Wildlife Resources","active":true,"usgs":false}],"preferred":false,"id":925512,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Klopfer, S.D.","contributorId":350239,"corporation":false,"usgs":false,"family":"Klopfer","given":"S.D.","affiliations":[{"id":25550,"text":"Virginia Polytechnic Institute and State University","active":true,"usgs":false}],"preferred":false,"id":925513,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Martin, B.","contributorId":350241,"corporation":false,"usgs":false,"family":"Martin","given":"B.","affiliations":[{"id":83699,"text":"Fort Pickett","active":true,"usgs":false}],"preferred":false,"id":925514,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ford, W. 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Traditionally, much of the research on bat ecology has focused on behavior of summer maternity colonies within species’ distribution cores, including that of the endangered northern long-eared bat (Myotis septentrionalis). To further our knowledge of this species, we evaluated multi-season activity patterns in eastern North Carolina and Virginia, including areas where populations were recently discovered. We used passive acoustic monitoring to assess relative and probable activity of northern long-eared bats from October 2016 to August 2021. Northern long-eared bat relative activity was greatest in areas containing greater proportions of woody wetlands and upland pine-dominated evergreen forests. However, the likelihood of recording northern long-eared bats was associated with smaller proportions of woody wetlands and open water resources. Furthermore, we observed a higher probability of recording northern long-eared bats during non-winter seasons. Probable activity was greatest at temperatures between 10 and 25 C, potentially highlighting an optimal thermoneutral zone for the species regionally. Relative activity of northern long-eared bats on the Coastal Plain of Virginia and North Carolina was primarily driven by cover features, whereas probable activity was driven by a combination of cover features, seasonality, and temperature. Therefore, acoustical surveys for this species may be most effective when targeting woody wetlands adjacent to upland forests, particularly upland pine-dominated evergreen stands, during moderate temperatures of non-winter seasons (1 April–15 November). Moreover, conservation of a diverse mosaic of woody wetlands juxtaposed by upland forests may promote both roosting and overwintering habitat, thereby enhancing overwintering survival, maternity colony establishment, and ultimately, successful reproduction of northern long-eared bats.
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Resources","active":true,"usgs":false}],"preferred":false,"id":910654,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Ford, W. Mark 0000-0002-9611-594X wford@usgs.gov","orcid":"https://orcid.org/0000-0002-9611-594X","contributorId":172499,"corporation":false,"usgs":true,"family":"Ford","given":"W. Mark","email":"wford@usgs.gov","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":false,"id":910655,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70264589,"text":"70264589 - 2024 - Surface elevation trends in North Carolina's coastal wetlands","interactions":[],"lastModifiedDate":"2025-03-17T14:31:11.336488","indexId":"70264589","displayToPublicDate":"2024-03-01T09:22:04","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"title":"Surface elevation trends in North Carolina's coastal wetlands","docAbstract":"Coastal wetlands exist in the narrow fringe between uplands and open water and consequently, are uniquely vulnerable to the impacts of sea level rise (SLR). Predictive models suggest that in the coming decades sea level rise will lead to substantial losses of coastal wetland habitat in North Carolina. Empirical measurements of wetland response to SLR are vital for understanding which wetland complexes are most immediately threatened, so that mitigation, adaptation, and conservation efforts can be prioritized accordingly. Surface Elevation Tables (SETs) provide high resolution measures of wetland elevation change that can meet this need.
The North Carolina SET Community of Practice (NC SET COP) is a voluntary and unfunded partnership among stakeholders who have either installed Surface Elevation Tables (SETs) in North Carolina coastal wetlands, or who rely on SET data. The NC SET COP was formed in 2018 to identify and map the locations of all SETs installed within North Carolina, increase collaboration among SET users, and support efforts to identify regional trends in North Carolina coastal wetland response to sea level rise. The data presented here represent the first synoptic analysis of elevation trend data collected by NC SET COP partners.
Of 132 SETs installed across North Carolina (NC), 33 recorded net losses in elevation over the entire record of measurement. Among the 99 SETs that recorded positive elevation change, 79 (80%) did not build elevation fast enough to keep pace with the average rate of SLR over the past 30 years. The story these data tell is clear: the majority of NC’s coastal wetlands are not keeping pace with SLR. These data also provide a spatially explicit understanding of which wetlands are most at risk, and as a result, the SET data can help guide the use of restoration efforts for maximum effectiveness.
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The eastern spotted skunk (Spilogale putorius) is considered a species of conservation concern in Virginia, where populations are restricted to spatially disjunct forest patches in the central Appalachian Mountains. We caught and radio-tagged eastern spotted skunks in the Appalachian Mountains of Virginia to assess whether current habitat fragmentation restricts skunk movements and hence distribution, potentially leading to isolation among habitat patches. Denning home range size (approximately 3.7 km2) in our study was smaller than those in other studies of eastern spotted skunks and excursive movements were primarily limited to core forested areas (>2 km2). Core forested areas were used more than non-forested and forest-edge areas. We conclude core forest area fragmentation limits eastern spotted skunk movement between mountain ridges. Increased forest patch connectivity may help prevent genetic and demographic isolation, reduce the likelihood of local extinctions, and facilitate colonization of suitable unoccupied areas.
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There are 4 protected species of sea turtle (loggerhead, green, Kemp’s ridley, and leatherback) commonly found along the east coast of the USA, our study area, and which require impact assessments. Data from 7 different survey organizations were used to create density surface models for the 4 sea turtle species utilizing 1.2 million km of line-transect surveys. A substantial portion (29.7%) of available sightings were not identified to the species level. Not including these sightings would underestimate density, so a conditional random forest model was used to assign unidentified sightings to species. Higher densities of loggerhead, green, and Kemp’s ridley sea turtles were predicted south of the Outer Banks in cool months, transitioning northwards in late spring to occupy seasonal neritic habitats. The highest leatherback densities were predicted off the coasts of Georgia and Florida. Leatherbacks were also predicted throughout offshore areas. The predicted distribution patterns generally matched satellite tracking and strandings data, indicating the models reproduced established seasonal movements. Surveys rarely detect sea turtles smaller than 40 cm, so these age classes are not represented. The models are the first for the study area to apply availability bias estimates developed in or near the study area and attempt to classify unidentified sightings to the species level, providing an updated, critical tool for conservation management along the eastern seaboard.
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Information on how to effectively manage lowland brushlands for birds and associations between breeding birds and local-scale vegetation structure and composition is lacking. We surveyed lowland brushlands from 2016–2018 in Minnesota, USA, to assess bird-habitat associations using avian point-count surveys and fixed-radius vegetation plots. We used Poisson regression models to assess the associations between breeding bird species richness, total abundance, and abundance of frequently detected species (using counts as an index for abundance) to woody stem density and height, patchiness of woody stem density, variation of woody stem height, and number of woody plant species. Sedge wrens (Cistothorus stellaris), the most abundant species, were negatively associated with multiple woody plant metrics and positively associated with patchiness. Common yellowthroats (Geothlypis trichas) were the second-most abundant species and associated with low-stature woody plants (<1 m based on average heights in study sites). Bird species richness, alder flycatchers (Empidonax alnorum), chestnut-sided warblers (Setophaga pensylvanica), swamp sparrows (Melospiza georgiana), veeries (Catharus fuscescens), and yellow warblers (Setophaga petechia) increased with woody vegetation height. Chestnut-sided warbler and Nashville warbler (Leiothlypis ruficapilla) abundances also increased with woody stem density. We suggest that managing lowland brushlands to promote diverse woody plant structure, including tall shrubs and areas with patchy, open herbaceous cover, by implementing temporally and spatially variable disturbance regimes, may benefit bird species that rely on lowland brushlands with a range of vegetation structure requirements.
","language":"English","publisher":"The Wildlife Society","doi":"10.1002/jwmg.22568","usgsCitation":"Hawkinson, A., Montgomery, R.A., Roy, C.L., Shartell, L., Andersen, D.E., Stevens, T.K., Knosalla, L., and Frelich, L.E., 2024, Bird-habitat associations and local-scale vegetation structure in lowland brushlands: Journal of Wildlife Management, v. 88, no. 4, e22568, 24 p., https://doi.org/10.1002/jwmg.22568.","productDescription":"e22568, 24 p.","ipdsId":"IP-141848","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":467027,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/jwmg.22568","text":"Publisher Index Page"},{"id":466421,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Minnesota","county":"Aitkin County, Carlton County, St. Louis County","otherGeospatial":"east-central Minnesota","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -93.55095209733292,\n 47.81578189067989\n ],\n [\n -93.55095209733292,\n 46.62374994560324\n ],\n [\n -92.13724027549586,\n 46.62374994560324\n ],\n [\n -92.13724027549586,\n 47.81578189067989\n ],\n [\n -93.55095209733292,\n 47.81578189067989\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"88","issue":"4","noUsgsAuthors":false,"publicationDate":"2024-02-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Hawkinson, Annie J","contributorId":348300,"corporation":false,"usgs":false,"family":"Hawkinson","given":"Annie J","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":923334,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Montgomery, Rebecca A.","contributorId":328437,"corporation":false,"usgs":false,"family":"Montgomery","given":"Rebecca","email":"","middleInitial":"A.","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":923335,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Roy, Charlotte L.","contributorId":274613,"corporation":false,"usgs":false,"family":"Roy","given":"Charlotte","email":"","middleInitial":"L.","affiliations":[{"id":6964,"text":"Minnesota Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":923336,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Shartell, Lindsey M.","contributorId":348301,"corporation":false,"usgs":false,"family":"Shartell","given":"Lindsey M.","affiliations":[{"id":6964,"text":"Minnesota Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":923337,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Andersen, David E. 0000-0001-9535-3404 dea@usgs.gov","orcid":"https://orcid.org/0000-0001-9535-3404","contributorId":199408,"corporation":false,"usgs":true,"family":"Andersen","given":"David","email":"dea@usgs.gov","middleInitial":"E.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":923338,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Stevens, Thomas K.","contributorId":333873,"corporation":false,"usgs":false,"family":"Stevens","given":"Thomas","email":"","middleInitial":"K.","affiliations":[{"id":38051,"text":"Western EcoSystems Technology, Inc.","active":true,"usgs":false}],"preferred":false,"id":923593,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Knosalla, Lori J.","contributorId":348574,"corporation":false,"usgs":false,"family":"Knosalla","given":"Lori J.","affiliations":[],"preferred":false,"id":923594,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Frelich, Lee E.","contributorId":179338,"corporation":false,"usgs":false,"family":"Frelich","given":"Lee","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":923339,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70263570,"text":"70263570 - 2024 - The 2023 US National Seismic Hazard Model: Ground-motion characterization for the conterminous United States","interactions":[],"lastModifiedDate":"2025-02-14T15:32:35.872261","indexId":"70263570","displayToPublicDate":"2024-02-29T08:19:01","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1436,"text":"Earthquake Spectra","active":true,"publicationSubtype":{"id":10}},"title":"The 2023 US National Seismic Hazard Model: Ground-motion characterization for the conterminous United States","docAbstract":"We update the ground-motion characterization for the 2023 National Seismic Hazard Model (NSHM) for the conterminous United States. The update includes the use of new ground-motion models (GMMs) in the Cascadia subduction zone; an adjustment to the central and eastern United States (CEUS) GMMs to reduce misfits with observed data; an updated boundary for the application of GMMs for shallow, crustal earthquakes in active tectonic regions (i.e. western United States (WUS)) and stable continental regions (i.e. CEUS); and the use of improved models for the site response of deep sedimentary basins in the WUS and CEUS. Site response updates include basin models for the California Great Valley and for the Portland and Tualatin basins, Oregon, as well as long-period basin effects from three-dimensional simulations in the Greater Los Angeles region and in the Seattle basin; in the CEUS, we introduce a broadband (0.01- to 10-s period) amplification model for the effects of the passive-margin basins of the Atlantic and Gulf Coastal Plains. In addition, we summarize progress on implementing rupture directivity models into seismic hazard models, although they are not incorporated in the 2023 NSHM. We implement the ground-motion characterization for the 2023 NSHM in the US Geological Survey’s code for probabilistic seismic hazard analysis, nshmp-haz-v2, and present the sensitivity of hazard to these changes. Hazard calculations indicate widespread effects from adjustments to the CEUS GMMs, from the incorporation of Coastal Plain amplification effects, and from the treatment of shallow-basin and out-of-basin sites in the San Francisco Bay Area and Los Angeles region, as well as locally important changes from subduction-zone GMMs, and from updated and new WUS basins.
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pmpowers@usgs.gov","orcid":"https://orcid.org/0000-0003-2124-6184","contributorId":176814,"corporation":false,"usgs":true,"family":"Powers","given":"Peter","email":"pmpowers@usgs.gov","middleInitial":"M.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":927391,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Rezaeian, Sanaz 0000-0001-7589-7893","orcid":"https://orcid.org/0000-0001-7589-7893","contributorId":238513,"corporation":false,"usgs":true,"family":"Rezaeian","given":"Sanaz","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":927392,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Shumway, Allison M. 0000-0003-1142-7141 ashumway@usgs.gov","orcid":"https://orcid.org/0000-0003-1142-7141","contributorId":147862,"corporation":false,"usgs":true,"family":"Shumway","given":"Allison","email":"ashumway@usgs.gov","middleInitial":"M.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":927393,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Smith, James Andrew 0000-0002-5565-9254 jimsmith@usgs.gov","orcid":"https://orcid.org/0000-0002-5565-9254","contributorId":332933,"corporation":false,"usgs":true,"family":"Smith","given":"James","email":"jimsmith@usgs.gov","middleInitial":"Andrew","affiliations":[{"id":78686,"text":"Geologic Hazards Science Center - Seismology / Geomagnetism","active":true,"usgs":true}],"preferred":true,"id":927394,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Stephenson, William J. 0000-0001-8699-0786 wstephens@usgs.gov","orcid":"https://orcid.org/0000-0001-8699-0786","contributorId":695,"corporation":false,"usgs":true,"family":"Stephenson","given":"William","email":"wstephens@usgs.gov","middleInitial":"J.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":927395,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Thompson, Eric M. 0000-0002-6943-4806 emthompson@usgs.gov","orcid":"https://orcid.org/0000-0002-6943-4806","contributorId":150897,"corporation":false,"usgs":true,"family":"Thompson","given":"Eric","email":"emthompson@usgs.gov","middleInitial":"M.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":927396,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Withers, Kyle B. 0000-0001-7863-3930","orcid":"https://orcid.org/0000-0001-7863-3930","contributorId":203492,"corporation":false,"usgs":true,"family":"Withers","given":"Kyle","middleInitial":"B.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":927397,"contributorType":{"id":1,"text":"Authors"},"rank":15}]}} ,{"id":70262418,"text":"70262418 - 2024 - Monarch butterfly (Danaus plexippus) roost site-selection criteria and locations east of the Appalachian Mountains, U.S.A.","interactions":[],"lastModifiedDate":"2025-01-23T17:25:02.725988","indexId":"70262418","displayToPublicDate":"2024-02-23T11:20:41","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":19915,"text":"Journal of Insect Behavior","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Monarch butterfly (Danaus plexippus) roost site-selection criteria and locations east of the Appalachian Mountains, U.S.A.","title":"Monarch butterfly (Danaus plexippus) roost site-selection criteria and locations east of the Appalachian Mountains, U.S.A.","docAbstract":"The monarch butterfly is a flagship species and pollinator whose populations have declined by 85% in the recent two decades. Their largest population overwinters in Mexico, then disperses across eastern North America during March to August. During September-December, they return south using two flyways, one that spans the central United States and another that follows the Atlantic coast. Migrating monarchs fly diurnally and roost in groups nocturnally. We sought to determine the criteria this species uses to select roost sites, and the landscape context where those sites are found. We developed species distribution models of the landscape context of Atlantic flyway roost sites via citizen scientist observations and environmental variables that affect monarchs in the adult stage prior to migration, using two algorithms (Maximum Entropy and Genetic Algorithm for Ruleset Prediction). We developed two model validation methods: a citizen scientist smartphone application and peer-informed comparisons with aerial imagery. Proximity to surface water, elevation, and vegetative cover were the most important criteria for monarch roost site selection. Our model predicted 2.6 million ha (2.9% of the study area) of suitable roosting habitat in the Atlantic flyway, with the greatest availability along the Atlantic coastal plain and Appalachian Mountain ridges. Conservation of this species is difficult, as monarchs range over both large areas and various habitat types, and most current monarch research and conservation efforts are focused on the breeding and overwintering periods. These models can serve to help prioritize surveys of roosting sites and conservation efforts during the monarchs’ fall migration.
","language":"English","publisher":"Springer","doi":"10.1007/s10905-023-09844-5","usgsCitation":"Boxler, B., Loftin, C., and Sutton, W.B., 2024, Monarch butterfly (Danaus plexippus) roost site-selection criteria and locations east of the Appalachian Mountains, U.S.A.: Journal of Insect Behavior, v. 37, p. 22-48, https://doi.org/10.1007/s10905-023-09844-5.","productDescription":"27 p.","startPage":"22","endPage":"48","ipdsId":"IP-122336","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":481012,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"37","noUsgsAuthors":false,"publicationDate":"2024-02-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Boxler, Brandon M.","contributorId":349226,"corporation":false,"usgs":false,"family":"Boxler","given":"Brandon M.","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":924154,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Loftin, Cyndy 0000-0001-9104-3724 cyndy_loftin@usgs.gov","orcid":"https://orcid.org/0000-0001-9104-3724","contributorId":146427,"corporation":false,"usgs":true,"family":"Loftin","given":"Cyndy","email":"cyndy_loftin@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":924153,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sutton, William B.","contributorId":88256,"corporation":false,"usgs":true,"family":"Sutton","given":"William","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":924155,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70263320,"text":"70263320 - 2024 - Monarch butterfly (Danaus plexippus) roost site-selection criteria and locations east of the Appalachian Mountains, U.S.A.","interactions":[],"lastModifiedDate":"2025-02-06T16:49:39.815475","indexId":"70263320","displayToPublicDate":"2024-02-23T10:46:37","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":19915,"text":"Journal of Insect Behavior","active":true,"publicationSubtype":{"id":10}},"title":"Monarch butterfly (Danaus plexippus) roost site-selection criteria and locations east of the Appalachian Mountains, U.S.A.","docAbstract":"The monarch butterfly is a flagship species and pollinator whose populations have declined by 85% in the recent two decades. Their largest population overwinters in Mexico, then disperses across eastern North America during March to August. During September-December, they return south using two flyways, one that spans the central United States and another that follows the Atlantic coast. Migrating monarchs fly diurnally and roost in groups nocturnally. We sought to determine the criteria this species uses to select roost sites, and the landscape context where those sites are found. We developed species distribution models of the landscape context of Atlantic flyway roost sites via citizen scientist observations and environmental variables that affect monarchs in the adult stage prior to migration, using two algorithms (Maximum Entropy and Genetic Algorithm for Ruleset Prediction). We developed two model validation methods: a citizen scientist smartphone application and peer-informed comparisons with aerial imagery. Proximity to surface water, elevation, and vegetative cover were the most important criteria for monarch roost site selection. Our model predicted 2.6 million ha (2.9% of the study area) of suitable roosting habitat in the Atlantic flyway, with the greatest availability along the Atlantic coastal plain and Appalachian Mountain ridges. Conservation of this species is difficult, as monarchs range over both large areas and various habitat types, and most current monarch research and conservation efforts are focused on the breeding and overwintering periods. These models can serve to help prioritize surveys of roosting sites and conservation efforts during the monarchs’ fall migration.
","language":"English","publisher":"Springer","doi":"10.1007/s10905-023-09844-5","usgsCitation":"Boxler, B., Loftin, C., and Sutton, W.B., 2024, Monarch butterfly (Danaus plexippus) roost site-selection criteria and locations east of the Appalachian Mountains, U.S.A.: Journal of Insect Behavior, v. 37, p. 22-48, https://doi.org/10.1007/s10905-023-09844-5.","productDescription":"27 p.","startPage":"22","endPage":"48","ipdsId":"IP-123861","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":481758,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"37","noUsgsAuthors":false,"publicationDate":"2024-02-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Boxler, Brandon M.","contributorId":349226,"corporation":false,"usgs":false,"family":"Boxler","given":"Brandon M.","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":926324,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Loftin, Cyndy 0000-0001-9104-3724 cyndy_loftin@usgs.gov","orcid":"https://orcid.org/0000-0001-9104-3724","contributorId":146427,"corporation":false,"usgs":true,"family":"Loftin","given":"Cyndy","email":"cyndy_loftin@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":926323,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sutton, William B.","contributorId":88256,"corporation":false,"usgs":true,"family":"Sutton","given":"William","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":926325,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70256504,"text":"70256504 - 2024 - Assessing grass carp (Ctenopharyngodon idella) occupancy and detection probability within Lake Erie from environmental DNA","interactions":[],"lastModifiedDate":"2024-09-09T17:01:27.882316","indexId":"70256504","displayToPublicDate":"2024-02-23T06:11:31","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2655,"text":"Management of Biological Invasions","active":true,"publicationSubtype":{"id":10}},"title":"Assessing grass carp (Ctenopharyngodon idella) occupancy and detection probability within Lake Erie from environmental DNA","docAbstract":"Grass carp (Ctenopharyngodon idella), an invasive cyprinid within the Laurentian Great Lakes, is naturally reproducing in several Lake Erie tributaries, which has raised concerns of the species’ spread throughout Lake Erie and the other Great Lakes. Knowledge of the recent invasion extent outside of the western basin of Lake Erie, particularly in eastern tributaries and nearshore waters, is limited. Understanding the invasion extent would improve the efficacy of ongoing coordinated multi-agency control efforts. Molecular tools, such as environmental DNA (eDNA), have shown promise for early detection of aquatic invasive species. In this study, water samples (N = 476) were collected for grass carp eDNA monthly between May and November in 2018 and 2019, at three sites in the Michigan waters of Lake Erie and the Detroit River. We fit Bayesian multi-scale occupancy models to determine differences in eDNA capture and detection probability among grass carp qPCR assays, sampling sites, and across time. To determine whether grass carp were physically present, and to validate eDNA samples, we quantified recent grass carp presence in sampled areas using an existing acoustic telemetry and field sampling framework. Our results indicate that grass carp eDNA capture probability differed among sites, but there was no difference among months. Positive grass carp eDNA detections were observed across multiple months at each site, with 69% of site-specific sampling events testing positive for grass carp eDNA on at least one assay and replicate. The majority (65%) of weeks where positive eDNA sampling detections occurred also concurrently had one or more grass carp detected via acoustic telemetry 1–6 days prior. Our results highlight the potential utility of using eDNA to monitor the invasion extent of grass carp within the nearshore waters of Lake Erie. However, further evaluation of the factors that influence grass carp eDNA characteristics among sites within Lake Erie are needed to determine its efficacy for surveillance protocols by natural resource management agencies.
","language":"English","publisher":"Reabic","doi":"10.3391/mbi.2024.15.1.04","usgsCitation":"Bopp, J., Nathan, L.R., Robinson, J.D., Kanefsky, J., Scribner, K., Herbst, S., and Robinson, K.F., 2024, Assessing grass carp (Ctenopharyngodon idella) occupancy and detection probability within Lake Erie from environmental DNA: Management of Biological Invasions, v. 15, no. 1, p. 51-72, https://doi.org/10.3391/mbi.2024.15.1.04.","productDescription":"22 p.","startPage":"51","endPage":"72","ipdsId":"IP-153893","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":440334,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3391/mbi.2024.15.1.04","text":"Publisher Index Page"},{"id":433635,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Lake Erie","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -83.49486489590893,\n 42.187220486618514\n ],\n [\n -83.49486489590893,\n 41.38443575807409\n ],\n [\n -82.35228677090853,\n 41.38443575807409\n ],\n [\n -82.35228677090853,\n 42.187220486618514\n ],\n [\n -83.49486489590893,\n 42.187220486618514\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"15","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Bopp, Justin","contributorId":340933,"corporation":false,"usgs":false,"family":"Bopp","given":"Justin","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":907700,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nathan, Lucas R.","contributorId":340934,"corporation":false,"usgs":false,"family":"Nathan","given":"Lucas","email":"","middleInitial":"R.","affiliations":[{"id":36986,"text":"Michigan Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":907701,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Robinson, John D.","contributorId":340936,"corporation":false,"usgs":false,"family":"Robinson","given":"John","email":"","middleInitial":"D.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":907702,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kanefsky, Jeanette","contributorId":340938,"corporation":false,"usgs":false,"family":"Kanefsky","given":"Jeanette","email":"","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":907703,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Scribner, Kim T.","contributorId":340939,"corporation":false,"usgs":false,"family":"Scribner","given":"Kim T.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":907704,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Herbst, Seth","contributorId":340940,"corporation":false,"usgs":false,"family":"Herbst","given":"Seth","affiliations":[{"id":36986,"text":"Michigan Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":907705,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Robinson, Kelly Filer 0000-0001-8109-9492","orcid":"https://orcid.org/0000-0001-8109-9492","contributorId":340631,"corporation":false,"usgs":true,"family":"Robinson","given":"Kelly","email":"","middleInitial":"Filer","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":907706,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70264388,"text":"70264388 - 2024 - Ensemble2: Scenarios ensembling for communication and performance analysis","interactions":[],"lastModifiedDate":"2025-03-14T14:40:51.762475","indexId":"70264388","displayToPublicDate":"2024-02-22T09:37:18","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5213,"text":"Epidemics","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Ensemble2: Scenarios ensembling for communication and performance analysis","title":"Ensemble2: Scenarios ensembling for communication and performance analysis","docAbstract":"Throughout the COVID-19 pandemic, scenario modeling played a crucial role in shaping the decision-making process of public health policies. Unlike forecasts, scenario projections rely on specific assumptions about the future that consider different plausible states-of-the-world that may or may not be realized and that depend on policy interventions, unpredictable changes in the epidemic outlook, etc. As a consequence, long-term scenario projections require different evaluation criteria than the ones used for traditional short-term epidemic forecasts. Here, we propose a novel ensemble procedure for assessing pandemic scenario projections using the results of the Scenario Modeling Hub (SMH) for COVID-19 in the United States (US). By defining a “scenario ensemble” for each model and the ensemble of models, termed “Ensemble
Landscape context structures fish abundance and dynamics, and understanding trends in fish abundance across the landscape is often prerequisite for effective conservation. In this study, we evaluated the status and trends of Brook Trout Salvelinus fontinalis in Shenandoah National Park to understand how these are structured across bedrock geology, elevation, and stream size.
We used long-term monitoring data from 94 sites in Shenandoah National Park to evaluate trends in Brook Trout abundance over a 27-year period (1996–2022) and assess the importance of local environmental covariates using a hierarchical Bayesian N-mixture model based on depletion sampling. Focal covariates were chosen for their demonstrated importance in structuring fish populations in Shenandoah National Park and elsewhere. Bedrock geology controls sensitivity to acid deposition, watershed area is related to stream habitat features such as complexity and flow variability, and elevation creates gradients in temperature.
Models revealed significant decreases in adult Brook Trout abundance over time (95% credible intervals < 0) for 31 of 94 sites (33%), and at least three sites exhibited apparent extirpations over the study period. Estimated Brook Trout abundance declined by 50% or more in approximately 70% of streams across the park over the study period. Sites with the warmest water temperatures exhibited the fastest declines in abundance. However, large watersheds on poorly buffered bedrock exhibited significant gains in abundance over time, suggesting some recovery from acid deposition due to improvements in air quality.
Our analysis revealed large and divergent changes in Brook Trout abundance over recent decades and suggests the importance of local water temperature and acid sensitivity as probable causal mechanisms. These results highlight the importance of considering local factors when evaluating long-term trends in stream fish populations. Results of this study can assist the development of targeted conservation actions within Shenandoah National Park and elsewhere.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/tafs.10460","usgsCitation":"Childress, E., Demarest, D., Wofford, J.E., Hitt, N.P., and Letcher, B., 2024, Strong variation in Brook Trout trends across geology, elevation, and stream size in Shenandoah National Park: Transactions of the American Fisheries Society, v. 153, no. 2, p. 250-263, https://doi.org/10.1002/tafs.10460.","productDescription":"14 p.","startPage":"250","endPage":"263","ipdsId":"IP-150752","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":499264,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/tafs.10460","text":"Publisher Index Page"},{"id":426485,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Virginia","otherGeospatial":"Shenandoah National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -79.5730072818129,\n 37.84539890269417\n ],\n [\n -77.41219734606564,\n 37.84539890269417\n ],\n [\n -77.41219734606564,\n 39.434228970300325\n ],\n [\n -79.5730072818129,\n 39.434228970300325\n ],\n [\n -79.5730072818129,\n 37.84539890269417\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"153","issue":"2","noUsgsAuthors":false,"publicationDate":"2024-02-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Childress, Evan S.","contributorId":214287,"corporation":false,"usgs":false,"family":"Childress","given":"Evan S.","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":896226,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Demarest, David E","contributorId":334666,"corporation":false,"usgs":false,"family":"Demarest","given":"David E","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":896227,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wofford, John E.B.","contributorId":334667,"corporation":false,"usgs":false,"family":"Wofford","given":"John","email":"","middleInitial":"E.B.","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":896228,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hitt, Nathaniel P. 0000-0002-1046-4568","orcid":"https://orcid.org/0000-0002-1046-4568","contributorId":238185,"corporation":false,"usgs":true,"family":"Hitt","given":"Nathaniel","email":"","middleInitial":"P.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":896229,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Letcher, Benjamin 0000-0003-0191-5678","orcid":"https://orcid.org/0000-0003-0191-5678","contributorId":242666,"corporation":false,"usgs":true,"family":"Letcher","given":"Benjamin","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":896230,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70252746,"text":"70252746 - 2024 - Nitrate exposure from drinking water and dietary sources among Iowa farmers using private wells","interactions":[],"lastModifiedDate":"2024-04-04T14:33:34.378743","indexId":"70252746","displayToPublicDate":"2024-02-16T09:16:23","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":17043,"text":"Science of the Total Envionrment","active":true,"publicationSubtype":{"id":10}},"title":"Nitrate exposure from drinking water and dietary sources among Iowa farmers using private wells","docAbstract":"Nitrate levels are increasing in water resources across the United States and nitrate ingestion from drinking water has been associated with adverse health risks in epidemiologic studies at levels below the maximum contaminant level (MCL). In contrast, dietary nitrate ingestion has generally been associated with beneficial health effects. Few studies have characterized the contribution of both drinking water and dietary sources to nitrate exposure. The Agricultural Health Study is a prospective cohort of farmers and their spouses in Iowa and North Carolina. In 2018–2019, we assessed nitrate exposure for 47 farmers who used private wells for their drinking water and lived in 8 eastern Iowa counties where groundwater is vulnerable to nitrate contamination. Drinking water and dietary intakes were estimated using the National Cancer Institute Automated Self-Administered 24-Hour Dietary Assessment tool. We measured nitrate in tap water and estimated dietary nitrate from a database of food concentrations. Urinary nitrate was measured in first morning void samples in 2018–19 and in archived samples from 2010 to 2017 (minimum time between samples: 2 years; median: 7 years). We used linear regression to evaluate urinary nitrate concentrations in relation to total nitrate, and drinking water and dietary intakes separately. Overall, dietary nitrate contributed the most to total intake (median: 97 %; interquartile range [IQR]: 57–99 %). Among 15 participants (32 %) whose drinking water nitrate concentrations were at/above the U.S. Environmental Protection Agency MCL (10 mg/L NO3-N), median intake from water was 44 % (IQR: 26–72 %). Total nitrate intake was the strongest predictor of urinary nitrate concentrations (R2 = 0.53). Drinking water explained a similar proportion of the variation in nitrate excretion (R2 = 0.52) as diet (R2 = 0.47). Our findings demonstrate the importance of both dietary and drinking water intakes as determinants of nitrate excretion.
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